Outsourcing Type

The engagements split across the four outsourcing types, from CXM (42 of 150 engagements, 28.00%) to KPO (31 of 150 engagements, 20.67%). Cancellation was close across types, from CXM (35.71% canceled, n = 42 engagements) to KPO (38.71% canceled, n = 31 engagements), with no difference under test (chi-square 0.08, p 0.994, Cramer’s V 0.02, n = 150 engagements). Customer satisfaction on active engagements was highest for BPO (mean 66.3, n = 23 engagements) and lowest for KPO (mean 60.6, n = 19 engagements), with no difference under test (Welch F 0.78, p 0.510, omega squared −0.004, n = 94 engagements). The engagement indicator mean ranged from CXM at the high end (mean 4.2, n = 42 engagements) to BPO at the low end (mean 3.7, n = 37 engagements), with no difference under test (Welch F 0.90, p 0.444, omega squared −0.003, n = 150 engagements). Intent to leave ranged from CXM at the low end (mean 6.5, n = 42 engagements) to ITO at the high end (mean 7.1, n = 40 engagements), with no difference under test (Welch F 0.47, p 0.703, omega squared −0.011, n = 150 engagements). The outsourcing type mix differed after Holm control by process family (chi-square 121.20, Holm p < 0.001, Cramer’s V 0.57).

One written response was coded to the indicator, from 1 of 850 respondents on 1 of 150 engagements. The single scored response was negative, with a score of −5. It was a secondary response.

The value was recorded once per engagement, so every share counts out of 150 engagements.

Distressed is not applicable, because the answer has no good or bad end on a scale.

The single response coded to the indicator was a secondary response. The response was written in answer to C135.

The indicator has no good or bad end. Under the coding rulebook’s rule for such indicators, the written score records the effect each response states on the engagement or on the respondent. The engagement field is “ENG:Outsourcing Type.” The frequency, scope and consequence a response states set the strength of its score. A score of +10 marks a response that describes “this attribute of the engagement as helping the engagement at its best.” A score of −10 marks a response that describes “this attribute as harming the engagement at its worst . . . .”

The distribution was reported as counts only, because fewer than 10 responses carried a score. The single scored response carried a score of −5.

Shares are of the answered engagements in each row, which is the base beside them. A row under 10 prints counts in place of shares.

Engagement families were tested on engagement answers, and respondent role and vendor type were tested on respondents. Process family differed in the answer mix after Holm control (chi-square 121.20, Holm p < 0.001, Cramer’s V 0.57). No difference held after Holm control for institution type, class of work and engagement status and lifecycle stage.

The share of engagements canceled on the study date, measured at engagement level (base 150 engagements, 56 canceled). The association is a descriptive contrast, inflated because canceled engagements were rated after cancellation, and is not predictive.

The measure by the engagement’s recorded answer.

The test across answer levels, in the study set and inside each segment. Levels under 10 engagements were left out of each test.

Cancellation in the study set did not differ across answer levels (chi-square 0.08, p 0.994, n = 150 engagements).

The satisfaction score recorded against each engagement on a 100-point scale. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

The measure by the engagement’s recorded answer.

The test across answer levels, in the study set and inside each segment. Levels under 10 engagements were left out of each test.

The measure in the study set did not differ across answer levels (Welch F 0.78, p 0.510, n = 94 engagements). The clean estimate ran inside active engagements only.

The mean of an engagement’s 100 indicator ratings, measured at engagement level (base 150 engagements). Where this indicator is one of the 100, the mean is recomputed without it.

The measure by the engagement’s recorded answer.

The test across answer levels, in the study set and inside each segment. Levels under 10 engagements were left out of each test.

The measure in the study set did not differ across answer levels (Welch F 0.90, p 0.444, n = 150 engagements).

The respondent’s stated likelihood of leaving the current job within 12 months, rated 1 to 10 (C019). Measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

The measure by the engagement’s recorded answer.

The test across answer levels, in the study set and inside each segment. Levels under 10 engagements were left out of each test.

The measure in the study set did not differ across answer levels (Welch F 0.47, p 0.703, n = 150 engagements).

Steiger’s z compares two correlations, and a categorical answer yields an effect size across levels rather than a correlation, so the pairs are not tested.

Not applicable. The field was recorded once per engagement, so every respondent in an engagement shared one value.

The bound is the ruled |r| < 0.30 expressed as variance explained (V15-R22). Equivalent to zero: cancellation rate, customer satisfaction, engagement indicator mean and intent to leave.

Every check held. A check holds when its test lands on the same side of 0.05 as the primary test.

Each of the four measures was computed from data this indicator does not contain, so each result is a finding rather than an arithmetic consequence.

Not applicable. A categorical field has no signed correlation, so it has no profile. Its per-level means carry the comparison.

The indicator’s twin, C032, records the same fact under the coding rulebook’s twin rule. The twin was coded in the single response coded to the indicator. The twin is left out of the counts that follow. The response coded to the indicator was also coded to “Rate your employer’s current supply of qualified agentic AI talent against what its engagements need” (C122, 1 response), “Rate how well your employer’s training programs build financial industry knowledge” (C125, 1 response) and “Rate the depth of your employer’s knowledge of agentic AI” (C131, 1 response). The theme “Human presented as agentic” of the coding rulebook was the only theme in the same response.

Outsourcing type showed no tested association with any of the four study measures. That result held under every robustness check run on this field (5 of 5 checks).

The indicator is an engagement field, so the written responses were not set against a rating. Written responses were voluntary. They recorded what the respondents who wrote reported, and how common it was is measured by the recorded values. The analysis of the written responses rested on 1 response, a base of anecdote strength.

The evidence is reported experience: what the delivery team said about its own engagement, gathered in one cross-section. The figures showed associations, not causes. Indicators about the financial institution were reported at second hand by vendor staff. Cancellation is a descriptive contrast, because canceled engagements were rated after they ended.

No limit applied beyond the standing limits of this block.

Fewer than 10 responses carried a score, so written sentiment was reported as counts, with no statistic. Coding was performed by AI coders under a fixed rulebook. No response coded to the indicator fell in the sample second-coded under rulebook 0.5, so agreement on whether a response was coded to the indicator could not be assessed. No response coded to the indicator was second-scored under rulebook 0.6, so agreement on the score could not be assessed.